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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_babies.wasp
Title produced by softwareExercise 1.13
Date of computationMon, 13 Oct 2008 03:30:23 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/13/t1223890263tjquh4tkpe0lg91.htm/, Retrieved Fri, 17 May 2024 02:43:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15597, Retrieved Fri, 17 May 2024 02:43:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact193

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Exercise 1.13] [Exercise 1.13 (Wo...] [2008-10-01 13:28:34] [b98453cac15ba1066b407e146608df68]
F   P     [Exercise 1.13] [Exercise 1.13] [2008-10-13 09:30:23] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-10-15 16:57:59 [Kevin Truyts] [reply] 
Als oplossing op de eerste vraag (a en b) kunnen we stellen dat de student door het uitvoeren van verscheidene calculaties tot de juiste oplossing is gekomen. Hij is er dan ook van overtuigd dat de populatie moet vergroot worden.
Uit de oplossing van de tweede vraag (c) kunnen we afleiden dat de student duidelijk weet dat er meer metingen/trekkingen dienen te gebeuren. Wat de student fout doet is dat hij de gegevens veranderd, namelijk het vergoten van het aantal geboorten per dag, terwijl men enkel het aantal dagen, meerdere jaren i.p.v. 1 jaar, dient te nemen.
Bij de derde vraag (d) zien we dat de student de juiste conclusie heeft getrokken dankzij de 2 meetingen. Het resultaat is bekomen met een beetje geluk want de student heet het aantal dagen niet vergroot waardoor de kans op afwijking groter is dan wanneer hij dit wel had gedaan.
Bij de laatste vraag (e) is de student het antwoord te ver gaan zoeken. Zo moest enkel het > - teken veranderd worden in een < - teken en mocht er voor de rest niets veranderd worden in de R code.
2008-10-17 08:52:20 [90714a39acc78a7b2ecd294ecc6b2864] [reply] 
De student heeft de vraag goed begrepen en is tot een juist resultaat gekomen maar de conclusie lijkt mij niet helemaal correct. Er werden twee nieuwe calculaties uitgevoerd en de resultaten wijzen uit dat de berekening niet echt nauwkeurig is. Ik denk dat de student in de conclusie bedoelt dat het aantal geboortes per dag in het kleine ziekenhuis te klein is om een nauwkeuriger resultaat te verkrijgen. Naar mijn mening gaat het eerder over de wet van de grote getallen die zegt dat de schatting nauwkeuriger wordt naarmate er meer simulaties worden uitgevoerd. Een mogelijke tip is de berekening meerdere malen uitvoeren en de resultaten te schikken van klein naar groot. Dan kan je als oplossing weergeven dat het resultaat ergens ligt tussen de kleinste en grootste waarde.
2008-10-19 10:38:17 [Wim Golsteyn] [reply] 
De student heeft de vraag goed begrepen en de berekening correct opnieuw uitgevoerd. De conclusie houdt wel steek, maar is niet helemaal het antwoord waarnaar we zochten. Het gaat hier namelijk om de wet van de grote getallen die er voor zorgt dat naarmate een berekening meermaals wordt uitgevoerd (= over een langere periode), de waarschijnlijkheid meer accuraat wordt.

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Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15597&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15597&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15597&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days365
Expected number of births in Large Hospital45
Expected number of births in Small Hospital15
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital8150
#Males births in Large Hospital8275
#Female births in Small Hospital2760
#Male births in Small Hospital2715
Probability of more than 60 % of male births in Large Hospital0.0821917808219178
Probability of more than 60 % of male births in Small Hospital0.145205479452055
#Days per Year when more than 60 % of male births occur in Large Hospital30
#Days per Year when more than 60 % of male births occur in Small Hospital53

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 365 \tabularnewline
Expected number of births in Large Hospital & 45 \tabularnewline
Expected number of births in Small Hospital & 15 \tabularnewline
Percentage of Male births per day(for which the probability is computed) & 0.6 \tabularnewline
#Females births in Large Hospital & 8150 \tabularnewline
#Males births in Large Hospital & 8275 \tabularnewline
#Female births in Small Hospital & 2760 \tabularnewline
#Male births in Small Hospital & 2715 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0821917808219178 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.145205479452055 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 30 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 53 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15597&T=1

[TABLE]
[ROW][C]Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)[/C][/ROW]
[ROW][C]Number of simulated days[/C][C]365[/C][/ROW]
[ROW][C]Expected number of births in Large Hospital[/C][C]45[/C][/ROW]
[ROW][C]Expected number of births in Small Hospital[/C][C]15[/C][/ROW]
[ROW][C]Percentage of Male births per day(for which the probability is computed)[/C][C]0.6[/C][/ROW]
[ROW][C]#Females births in Large Hospital[/C][C]8150[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]8275[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]2760[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]2715[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0821917808219178[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.145205479452055[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]30[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]53[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15597&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15597&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days365
Expected number of births in Large Hospital45
Expected number of births in Small Hospital15
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital8150
#Males births in Large Hospital8275
#Female births in Small Hospital2760
#Male births in Small Hospital2715
Probability of more than 60 % of male births in Large Hospital0.0821917808219178
Probability of more than 60 % of male births in Small Hospital0.145205479452055
#Days per Year when more than 60 % of male births occur in Large Hospital30
#Days per Year when more than 60 % of male births occur in Small Hospital53


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
numsuccessbig <- 0
numsuccesssmall <- 0
bighospital <- array(NA,dim=c(par1,par2))
smallhospital <- array(NA,dim=c(par1,par3))
bigprob <- array(NA,dim=par1)
smallprob <- array(NA,dim=par1)
for (i in 1:par1) {
bighospital[i,] <- sample(c('F','M'),par2,replace=TRUE)
if (as.matrix(table(bighospital[i,]))[2] > par4*par2) numsuccessbig = numsuccessbig + 1
bigprob[i] <- numsuccessbig/i
smallhospital[i,] <- sample(c('F','M'),par3,replace=TRUE)
if (as.matrix(table(smallhospital[i,]))[2] > par4*par3) numsuccesssmall = numsuccesssmall + 1
smallprob[i] <- numsuccesssmall/i
}
tbig <- as.matrix(table(bighospital))
tsmall <- as.matrix(table(smallhospital))
tbig
tsmall
numsuccessbig/par1
bigprob[par1]
numsuccesssmall/par1
smallprob[par1]
numsuccessbig/par1*365
bigprob[par1]*365
numsuccesssmall/par1*365
smallprob[par1]*365
bitmap(file='test1.png')
plot(bigprob,col=2,main='Probability in Large Hospital',xlab='#simulated days',ylab='probability')
dev.off()
bitmap(file='test2.png')
plot(smallprob,col=2,main='Probability in Small Hospital',xlab='#simulated days',ylab='probability')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of simulated days',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Expected number of births in Large Hospital',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Expected number of births in Small Hospital',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Percentage of Male births per day
(for which the probability is computed)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Females births in Large Hospital',header=TRUE)
a<-table.element(a,tbig[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Males births in Large Hospital',header=TRUE)
a<-table.element(a,tbig[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Female births in Small Hospital',header=TRUE)
a<-table.element(a,tsmall[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Male births in Small Hospital',header=TRUE)
a<-table.element(a,tsmall[2])
a<-table.row.end(a)
a<-table.row.start(a)
dum1 <- paste('Probability of more than', par4*100, sep=' ')
dum <- paste(dum1, '% of male births in Large Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, bigprob[par1])
a<-table.row.end(a)
dum <- paste(dum1, '% of male births in Small Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, smallprob[par1])
a<-table.row.end(a)
a<-table.row.start(a)
dum1 <- paste('#Days per Year when more than', par4*100, sep=' ')
dum <- paste(dum1, '% of male births occur in Large Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, bigprob[par1]*365)
a<-table.row.end(a)
dum <- paste(dum1, '% of male births occur in Small Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, smallprob[par1]*365)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')